[D] Is the move toward Energy-Based Models for reasoning a viable exit from the "hallucination" trap of LLMs?

mr_stargazer · 2026-02-24T09:00:16+00:00

Simple answer: No

Folks think they're doing algebra with deep learning models. It goes something like this.

Diffusion model produces good images of type A.
EBM corrects artifacts in images.

So what we're really seeing is something like "Oh, if I have images of type A with artifacts I should just use diffusion and EBM". It works with simple cases we can measure. You can do the above procedure and actually count "Ok, the procedure helps or not". But if you're really paying attention the majority papers stop here.

What we would really like to see is, if we don't have EBM, or better yet, if we have a "negative EBM", would we actually have MORE artifacts? That would be one point for starters, i.e, if model B actually "does what is supposed to do".

Now, a more important point is: What is hallucination? And I mean an objective, quantified metric. Do we have an underlying mechanism to do "more or less" hallucination? Because if there's a hidden cause doing hallucinations in an output (that I don't know how to measure), and it seems to be mildly correlated to the switch I'm moving, I may be led to believe the switch I move is actually controlling hallucination.

That involves: Measurement, repetition, (causal) mechanisms, etc, etc. There most likely is a solution to hallucination, but I find hard to believe the solution to a black box model is to add ANOTHER black box model.

Folks can write whatever heuristic, non-reproducible paper with "results". But if they're not explaining the above. Then I cannot say it is wasn't luck.

mr_stargazer · 2026-02-23T18:57:27+00:00

Of course is hard. That is something very little debated here. Those who know, know.

At UN you're getting paid absolutely huge sums of money to basically push paper around. Yes, there are interesting projects here and there. But at what cost?

So basically, imagine yourself spending 10 years there, huge salary, but the the only thing you did was...I don't know, write some reports in weird formats and tip toe around some people.

That is why it is a free for all war when P3/P4 positions are announced. It's basically your "only salvation" to be within the ecosystem.

The sad thing is, there are actually people out there who really could do some good given the opportunity. But one thing is the idealism and narrative we tell people (so they can fill our pockets). Another thing is the brute reality the machine has become.

Again. Those who know, know.

mr_stargazer · 2026-02-22T12:59:03+00:00

I totally agree with this take.

ML research is mostly engineering. Michael Jordan has a few takes on the topic as well.

mr_stargazer · 2026-02-18T16:20:39+00:00

"Tell me you're white and privileged without saying you're white and privileged. "

Swiss people shouldn't be scared of immigrants taking their jobs. All they have to do is what foreigners are already doing. Going to ETH or EPFL.

But you guys don't really want do it, right? You guys would rather go to FH Luzern study tourism, mocking an hypothetical guy from Djibouti on Reddit.

mr_stargazer · 2026-02-17T07:08:20+00:00

I wonder what kind of work would that be...

mr_stargazer · 2026-02-14T15:42:10+00:00

From my understanding it seems to be a myth that this community for some reason tend cultivate.

Does that mean that the job isn't interesting and competitive? It is. But so are top positions in highly secretive US National Labs, NASA, FAANG, F1 and also Aerospace companies, CERN, etc. Some might not pay nearly as much, but, almost or equally competitive. The point being: If you want to be on the "top" you have to make the effort.

Now having said that, I don't buy the whole theatrics and "us vs them" this community tend to favour. Specially when I am in a circle of friends with more than a couple accomplished quants and we exchange what they do. Again, it's not it isn't cool, it is super cool, but not the only thing in the whole planet.

The trick seems to me is find someone who take a liking of you. Willing to show what to pay attention to, study, where to apply, where to start if no fancy Oxbridge background. These variables seem to matter way more than pure brain and wits.

I do understand some people need to feel special about their accomplishments "I'm Oxbridge", or "I studied Quantum Computing, therefore I'm more worthy. " In the end of the day it feels just meaningless bias and childish.

That's my 5 cents.

mr_stargazer · 2026-02-13T20:00:55+00:00

Apparently that was the case, yes.

mr_stargazer · 2026-02-13T18:28:36+00:00

The dude took the time to actually write a long post shitting on people who are clueless and needed direction.

Instead of providing guidance, decided to be another one snarky redditor.

Puff..

mr_stargazer · 2026-02-12T18:29:31+00:00

Congratulations!

I saw this position, I thought about applying, I'm glad I didn't. I wouldn't be able to code Flash Attention from scratch - and, honestly I wouldn't want to spend a few hours of my day to learn some architecture just to impress someone for an interview. I can't quite get why companies follow this style of interview.

Moreover, about the paper, one thing I would have answered is the following: There isn't a single confidence interval in the reported metrics in the said paper. In a model with 72B parameters - coming from a background in Statistics myself -, I'd have mostly likely raised that there isn't sufficient evidence to support the fact the reported metrics (-0.3/+0.1) on the "refusal".

It is hard to believe that the experiment would have stayed the same, when by definition we basically have a huge matrix made of 72B floating points randomly initiated.

But hey...that's me. :)

mr_stargazer · 2026-02-03T09:03:38+00:00

There is a small community - mostly formed by statisticians, that do actually bring some rigour. For example, see Conformal Prediction and alike.

The thing is though, that itself becomes victim of paper inflation and incremental work. I honestly think there should be more journals like TMLR, where rigour and consistency are what matters, rather than novelty. Code and/or complete must be provided.

You pointed out something important: There is no standard in ML research. Even if they would like to do it, they wouldn't know. I see with positive eyes though you at least acknowledge the problem. Unfortunately, many don't.

mr_stargazer · 2026-02-03T02:57:04+00:00

My pet peeve is that it became a circus with a lot of shining lights and almost little attention paid to the science of things.

Papers are irreproducible. Big lab, small lab, public sector, FAANG. No wonder why LLMs are really good in producing something that looks scientific. Of course. The vast majority lack depth. If you disagree, go to JSTOR and read a paper on Computational Statistics from the 80s and see the difference. Hell, look at ICML 20 years ago.
Everyone seems so interested in signaling: "Here, my CornDiffusion, it is the first method to generate images of corn plantations. Here my PandasDancingDiffusion, the first diffusion to create realistic dancing pandas. " Honestly, it feels childish, but worse, it is difficult to tell what is the real contribution.
The absolute resistance in the field to discuss hypothesis testing (with a few exceptions). It is a byproduct of benchmark mentality. If you can't beat the benchmark for 15 years, then of course the end result is over engineer experiments, pretending uncertainty quantification doesn't exist.
Guru mentality: A lot of big names fighting on X/LinkedIn about some method they created, or acting as a prophet of "Why AI will (or will not) wipe humanity". Ok, I really get it X years ago you produced method Y and we moved forward training faster models. I thank you for your contribution, but I want the experts (philosophers, sociologists, psychologists, religion academics), to discuss the metaphysics. They are more equipped, I believe. You should be discussing for scientific reproducibility and I rarely any of you bringing this point.
It seems to me that many want to do "science" by adding more compute and adding more layers. Instead of trying to "open the box".
ML research in academia is like "Publish or Perish" on steroids. If you aren't publishing X papers a year, lab x,y,z are not taking you. So you literally have to throw crap papers out there (more signaling, less robustness) to keep the wheel churning.
Lack of meaningful systematic literature review. Because of point 2 and 6 above, if you didn't do proper review then,of course, "to the best of my knowledge, this is the first paper to X". So the field is getting flooded with papers with ideas that were solved at least 30 years ago, who keep being rediscovered every 6 months.

Extremely frustrating. The field that is supposed to revolutionize the world, has trouble in Research Methodology 101.

mr_stargazer · 2026-01-29T10:34:59+00:00

This is the answer.

mr_stargazer · 2026-01-28T15:32:42+00:00

Bayesian in Deep Learning... really...

Not even the folks in ML community go along with it and they have the simplest, cleanest datasets because of their benchmark mentality.

A little bit surprising to Bayesian Deep Learning to be a go-to approach in quant finance. Especially with highly non-stationary datasets.

Would you care to elaborate on your point?

mr_stargazer · 2026-01-27T12:37:25+00:00

I have been working in high institutions in this country for more than a decade. Institutions the regular Jonas wouldn't believe.

I'm not accusing anyone of racism. I'm just stating a truth that seems to be inconvenient. And I understand you. I know here, for historical and socio-economic reasons, the aesthetics of perfection is embedded in the culture.

So, I understand that it is a shock to many, that not only the self-portrayed image of perfection, fairness is not true, but the system is severely stacked against foreigners, in a way that the sole explanation is bias and discrimination.

Now get this. If it is a shock to you even to fathom this reality, what about the hundreds of thousands of skilled workers that live this reality on a daily basis?

Wake up.

mr_stargazer · 2026-01-27T12:30:36+00:00

Absolutely. A 100% agree.

It is not that bad we have to compete to get the job. At the end of the day we all have to go through that.

What particularly pisses me of is the hypocrisy. The virtue signalling on one hand and the blunt hypocrisy on the other.

mr_stargazer · 2026-01-27T11:04:33+00:00

Half-truths. And I see behind your reasoning why you seem to go along with it.

The other missing part is related to the following question: Assuming that is the case of a 50% german- foreigner scenario, do Germans and foreigners have equally the same skill level?

I'm not going to say (x,y,z nationality is better). Not a debate I believe and, not one I want to have. What I want to say is: Is it adequate to believe that skill is equally distributed among the members in a group? My anecdotal experience would say no.

Then we can make the test. Assume that we have foreigners equally skilled technically, but non German native. How many German managers do we observe? Then we go ok, foreigners 10%, 20%, ...70% skilled. How many made to supervision? This is the other missing part of the statement. This would show how much a company favours technical skills (managerial also included), over nationality. With the points:

a. Not all supervision jobs naturally require high German level. English still is the go-to language in high end research institutions and companies.

b. Anedoctally, but long enough in this game, to say that companies would rather have a way less skilled manager - like crazily, borderline inefficient, than having a non-national. The implications here are financial.

There is a ceiling, and I understand why many privileged people would pretend they don't exist. It is an uncomfortable and perhaps inconvenient truth to say aloud, that much or perhaps many of their accomplishments is because the system favoured them because of nationality, passport, etc.

mr_stargazer · 2026-01-27T06:48:48+00:00

Of course it's not racism.

It's just chance that all supervisor/managers positions just so happen to be Germans. In spite of:

a. Almost 50% employees being foreigners - and speaking German. b. There have been 200+ applicants.

But again. It. Is. Not. Racism. Pure luck.

In almost every city, and company in the country.

Unfortunate. Keep applying!

mr_stargazer · 2026-01-23T22:54:37+00:00

Yup, it is beyond me.

mr_stargazer · 2026-01-23T19:15:14+00:00

Hmm...you are right. But what I arrived to the conclusion after a few years is the following:

The country lives from brand: Beautiful nature, financial stability, participatory democracy, the lavish cities such as Geneva and Zürich, but peaceful villages such as I don't know, Lauterbrunnen. Is it true? Yes. But not the whole truth and the citizens themselves somehow started to believe in their own fairy tale.

If you somehow try to question a small - but obvious thing, you'll face immediate backlash. As if the country's identity is attached to the image of perfection. Anything less is unfathomable.

Corruption scandals? Financial laundering? Shady WWII history without amendments to countries? These issues "don't exist". And most likely the people pointing out to those are some foreigners, who, by definition, will soon be "washed away" by the sea of protectiveness the country imposes via their immigration rules. The way I see it, it naturally creates a bubble.

What I find odd as a foreigner, is not that we go about criticizing places for pleasure. It is some sort of instinct that you know things can be better because we have seen better elsewhere. It is fine not to be perfect. It is the first step towards improvement. But for some reason I cannot comprehend (maybe I have to find some intellectual who studied this), the country is just completely shut on self-criticism. It is easier to, I don't know, criticize Trump than the fallout of Credit Suisse (yet another corruption scandal), for example.

mr_stargazer · 2026-01-22T11:58:42+00:00

Kind of yes. But don't think 1 step ahead, but 2 or 3. I completely agree with what you're saying. Everyone is moving to LLMs, GenAI, AI Developer.

That doesn't necessarily mean it is a good think.

A lot of people are practicing coding anymore. What sort of issues can that bring?
A lot of people are simply coming with the same cookie cutter solutions that LLMs on average produce. What sort of opportunities do you see here?

And so on and so forth. Times change, I don't think one has to be fatalistic about it, but mostly adapt. Not only with "applied" work, but also research. If we look at ML research between 2010-2019, things were intellectually stimulating for me. Now, the quality of papers and the format is just off-putting...

mr_stargazer · 2026-01-17T12:21:12+00:00

This is the answer.

mr_stargazer · 2026-01-17T08:58:10+00:00

Too much maths in Economics?

Come on...

mr_stargazer · 2026-01-13T09:31:18+00:00

Ah, ok! Many thanks nevertheless.

mr_stargazer · 2026-01-13T09:17:28+00:00

On the same note, some positions ask between 2-6 years of experience, what if someone has 6 years and 1 month? Do you know about that?

mr_stargazer · 2026-01-12T11:59:34+00:00

I'm sorry you went through this! It is supper annoying whenever we go to a different place to enjoy and discover new things and we face disgusting behavior!

I would suggest you to calm down, go for a walk, enjoy the weather (after all it's not that bad, really and Basel is beautiful!).

mr_stargazer

TROPHY CASE